Heatmap to visualize mutational spectra across all samples
These plots shows the total number of observations in each subtype, combined across all samples
Describes how each mutation subtype is loaded into the r signatures
## # A tibble: 3 x 4
## rowname S1 S2 S3
## <chr> <dbl> <dbl> <dbl>
## 1 S1 NA 0.9783121 0.8396949
## 2 S2 0.9783121 NA 0.7716099
## 3 S3 0.8396949 0.7716099 NA
## `mutate_each()` is deprecated.
## Use `mutate_all()`, `mutate_at()` or `mutate_if()` instead.
## To map `funs` over a selection of variables, use `mutate_at()`
Proportion each signature contributes to the mutation spectrum in each individual sample
Proportion each signature contributes to the mutation spectrum in each individual sample
Proportion each signature contributes to the mutation spectrum in each individual sample
This plot shows detailed information about the top 50 IDs with the largest root mean squared error. The left panel shows the RMSE values, and each sample is colored by the mutation signature with the largest contribution.
The contributions of each mutation signature for each of these samples are broken down in the right panel. If the sample was identified as an outlier (by having a signature contribution >2 standard deviations from the mean), it is marked with a star.
IDs with similar error profiles are grouped together using hierarchical clustering.
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()